cluster = parallel::makeCluster(rep("localhost", parallel::detectCores()), type = "SOCK")
experiment = Rtrack::read_experiment("Experiment_description2.xlsx", format = "Excel", cluster = cluster)
## Warning in read.table(file = file, header = header, sep = sep, quote = quote, :
## unvollstädige letzte Zeile von readTableHeader in './Arena_SW.txt' gefunden
## Warning in read.table(file = file, header = header, sep = sep, quote = quote, :
## unvollstädige letzte Zeile von readTableHeader in './Arena_RV.txt' gefunden
#Closing multicore cluster to reduce system load
parallel::stopCluster(cluster)
These metrics can be compared and extracted from the pure track files.
experiment$summary.variables
## [1] "path.length" "mean.velocity"
## [3] "sd.velocity" "total.time"
## [5] "latency.to.goal" "goal.crossings"
## [7] "old.goal.crossings" "coverage"
## [9] "mean.d.centroid" "mean.d.goal"
## [11] "mean.d.old.goal" "mean.d.origin"
## [13] "sd.d.centroid" "sd.d.goal"
## [15] "sd.d.old.goal" "sd.d.origin"
## [17] "centroid.goal.displacement" "centroid.old.goal.displacement"
## [19] "mean.initial.heading.error" "initial.trajectory.error"
## [21] "initial.reversal.error" "turning"
## [23] "turning.absolute" "efficiency"
## [25] "roaming.entropy" "time.in.zone.pool"
## [27] "time.in.zone.wall" "time.in.zone.far.wall"
## [29] "time.in.zone.annulus" "time.in.zone.goal"
## [31] "time.in.zone.old.goal" "time.in.zone.n.quadrant"
## [33] "time.in.zone.e.quadrant" "time.in.zone.s.quadrant"
## [35] "time.in.zone.w.quadrant"
par(mfrow = c(2, 2))
Rtrack::plot_variable("path.length", experiment = experiment, factor = "Strain", exclude.probe = TRUE,lwd = 2)
Rtrack::plot_variable("path.length", experiment = experiment, factor = "Age_group", exclude.probe = TRUE,lwd = 2)
Rtrack::plot_variable("path.length", experiment = experiment, factor = "Housing", exclude.probe = TRUE,lwd = 2)
Rtrack::plot_variable("path.length", experiment = experiment, factor = "All", exclude.probe = TRUE,lwd = 2)
wt.metrics = experiment$metrics[experiment$factors$Strain == "WT" &
(experiment$factors$`_Day` == 1 | experiment$factors$`_Day` == 2 | experiment$factors$`_Day` == 3 | experiment$factors$`_Day` == 4|
experiment$factors$`_Day` == 5| experiment$factors$`_Day` == 6)]
dTg.metrics = experiment$metrics[experiment$factors$Strain == "dTg" &
(experiment$factors$`_Day` == 1 | experiment$factors$`_Day` == 2 | experiment$factors$`_Day` == 3 | experiment$factors$`_Day` == 4| experiment$factors$`_Day` == 5| experiment$factors$`_Day` == 6)]
APP.metrics = experiment$metrics[experiment$factors$Strain == "APPswe" &
(experiment$factors$`_Day` == 1 | experiment$factors$`_Day` == 2 | experiment$factors$`_Day` == 3 | experiment$factors$`_Day` == 4| experiment$factors$`_Day` == 5| experiment$factors$`_Day` == 6)]
PS1.metrics = experiment$metrics[experiment$factors$Strain == "PS1dE9" &
(experiment$factors$`_Day` == 1 | experiment$factors$`_Day` == 2 | experiment$factors$`_Day` == 3 | experiment$factors$`_Day` == 4| experiment$factors$`_Day` == 5| experiment$factors$`_Day` == 6)]
par(mfrow = c(2, 2))
Rtrack::plot_density(wt.metrics, title = "wt Heatmap",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(dTg.metrics, title = "dTg Heatmap",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(APP.metrics, title = "APPswe Heatmap",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(PS1.metrics, title = "PS1dE9 Heatmap",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
wtr.metrics = experiment$metrics[experiment$factors$Strain == "WT" &
(experiment$factors$`_Day` == 7 | experiment$factors$`_Day` == 8 | experiment$factors$`_Day` == 9 | experiment$factors$`_Day` == 10)]
dTgr.metrics = experiment$metrics[experiment$factors$Strain == "dTg" &
(experiment$factors$`_Day` == 7 | experiment$factors$`_Day` == 8 | experiment$factors$`_Day` == 9 | experiment$factors$`_Day` == 10)]
APPr.metrics = experiment$metrics[experiment$factors$Strain == "APPswe" &
(experiment$factors$`_Day` ==7 | experiment$factors$`_Day` == 8 | experiment$factors$`_Day` == 9 | experiment$factors$`_Day` == 10)]
PS1r.metrics = experiment$metrics[experiment$factors$Strain == "PS1dE9" &
(experiment$factors$`_Day` == 7 | experiment$factors$`_Day` == 8 | experiment$factors$`_Day` == 9 | experiment$factors$`_Day` == 10)]
par(mfrow = c(2, 2))
Rtrack::plot_density(wtr.metrics, title = "WT_reversal Heatmap",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(dTgr.metrics, title = "dTg_reversal Heatmap",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(APPr.metrics, title = "APPswe_reversal Heatmap",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(PS1r.metrics, title = "PS1dE9_reversal Heatmap",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
strategies = Rtrack::call_strategy(experiment$metrics)
limits called strategies to those, where confidence is greater than 40%
dim(Rtrack::threshold_strategies(strategies, 0.4)$calls)
## [1] 4445 12
par(mfrow = c(2, 2))
Rtrack::plot_strategies(strategies, experiment = experiment, factor = "Strain",
exclude.probe = TRUE)
par(mfrow = c(2, 2))
Rtrack::plot_strategies(Rtrack::threshold_strategies(strategies, 0.4), experiment = experiment,
factor = "Strain", exclude.probe = TRUE)
Here we export the results of the analyzed Track Files into a data.frame, to analyse them further.
results = Rtrack::export_results(experiment)
datatable(results, rownames = FALSE, filter="top", options = list(pageLength = 5, scrollX=T) )
## Warning in instance$preRenderHook(instance): It seems your data is too big
## for client-side DataTables. You may consider server-side processing: https://
## rstudio.github.io/DT/server.html
library(ggplot2)
library(readxl)
Results=read_excel("Results2.xlsx")
ggplot(Results, aes(x=`_Day`,y=path.length,color=factor(Strain)))+geom_jitter()
Results
WT_STD=filter(Results, Strain=='WT'&Housing=='STD')
dTg_STD=filter(Results, Strain=='dTg'&Housing=='STD')
APP_STD=filter(Results, Strain=='APPswe'&Housing=='STD')
PS_STD=filter(Results, Strain=='PS1dE9'&Housing=='STD')
WT_ENR=filter(Results, Strain=='WT'&Housing=='ENR')
dTg_ENR=filter(Results, Strain=='dTg'&Housing=='ENR')
APP_ENR=filter(Results, Strain=='APPswe'&Housing=='ENR')
PS_ENR=filter(Results, Strain=='PS1dE9'&Housing=='ENR')
par(mfrow = c(2, 4))
WT_STD %>%
mutate(WT_STD, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=path.length, fill=Age_group))+geom_boxplot()+
labs(x="Day",
y="Average Path length",
title="Mean Path length WT STD")+scale_fill_jco()
dTg_STD %>%
mutate(dTg_STD, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=path.length, fill=Age_group))+geom_boxplot()+
labs(x="Day",
y="Average Path length",
title="Mean Path length dTg STD")+scale_fill_jco()
APP_STD %>%
mutate(APP_STD, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=path.length, fill=Age_group))+geom_boxplot()+
labs(x="Day",
y="Average Path length",
title="Mean Path length APPswe1 STD")+scale_fill_jco()
PS_STD %>%
mutate(PS_STD, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=path.length, fill=Age_group))+geom_boxplot()+
labs(x="Day",
y="Average Path length",
title="Mean Path length PS1dE9 STD")+scale_fill_jco()
WT_ENR %>%
mutate(WT_ENR, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=path.length, fill=Age_group))+geom_boxplot()+
labs(x="Day",
y="Average Path length",
title="Mean Path length WT ENR")+scale_fill_jco()
dTg_ENR %>%
mutate(dTg_ENR, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=path.length, fill=Age_group))+geom_boxplot()+
labs(x="Day",
y="Average Path length",
title="Mean Path length dTg ENR")+scale_fill_jco()
APP_ENR %>%
mutate(APP_ENR, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=path.length, fill=Age_group))+geom_boxplot()+
labs(x="Day",
y="Average Path length",
title="Mean Path length APPswe1 ENR")+scale_fill_jco()
PS_ENR %>%
mutate(PS_ENR, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=path.length, fill=Age_group))+geom_boxplot()+
labs(x="Day",
y="Average Path length",
title="Mean Path length PS1dE9 ENR")+scale_fill_jco()+
facet_wrap(~Age_group)
WT_STD %>%
mutate(WT_STD, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=latency.to.goal, fill=Age_group))+geom_boxplot()+
labs(x="Day",
y="Average Latency in s",
title="Mean Latency in s WT STD")+scale_fill_jco()
## Warning: Removed 587 rows containing non-finite values (stat_boxplot).
dTg_STD %>%
mutate(dTg_STD, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=latency.to.goal, fill=Age_group))+geom_boxplot()+
labs(x="Day",
y="Average Latency in s",
title="Mean Latency in s dTg STD")+scale_fill_jco()
## Warning: Removed 494 rows containing non-finite values (stat_boxplot).
APP_STD %>%
mutate(APP_STD, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=latency.to.goal, fill=Age_group))+geom_boxplot()+
labs(x="Day",
y="Average Latency in s",
title="Mean Latency in s APPswe1 STD")+scale_fill_jco()
## Warning: Removed 315 rows containing non-finite values (stat_boxplot).
PS_STD %>%
mutate(PS_STD, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=latency.to.goal, fill=Age_group))+geom_boxplot()+
labs(x="Day",
y="Average Latency in s",
title="Mean Latency in s PS1dE9 STD")+scale_fill_jco()
## Warning: Removed 285 rows containing non-finite values (stat_boxplot).
WT_ENR %>%
mutate(WT_ENR, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=latency.to.goal, fill=Age_group))+geom_boxplot()+
labs(x="Day",
y="Average Latency in s",
title="Mean Latency in s WT ENR")+scale_fill_jco()
## Warning: Removed 476 rows containing non-finite values (stat_boxplot).
dTg_ENR %>%
mutate(dTg_ENR, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=latency.to.goal, fill=Age_group))+geom_boxplot()+
labs(x="Day",
y="Average Latency in s",
title="Mean Latency in s dTg ENR")+scale_fill_jco()
## Warning: Removed 542 rows containing non-finite values (stat_boxplot).
APP_ENR %>%
mutate(APP_ENR, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=latency.to.goal, fill=Age_group))+geom_boxplot()+
labs(x="Day",
y="Average Latency in s",
title="Mean Latency in s APPswe1 ENR")+scale_fill_jco()
## Warning: Removed 262 rows containing non-finite values (stat_boxplot).
PS_ENR %>%
mutate(PS_ENR, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=latency.to.goal, fill=Age_group))+geom_boxplot()+
labs(x="Day",
y="Average Latency in s",
title="Mean Latency in s PS1dE9 ENR")+scale_fill_jco()+
facet_wrap(~Age_group)
## Warning: Removed 242 rows containing non-finite values (stat_boxplot).
par(mfrow = c(2, 2))
Rtrack::plot_density(wtp.metrics, title = "WT Probe Heatmap 3-25mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(dTgp.metrics, title = "dTg Probe Heatmap 3-25mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(APPp.metrics, title = "APPswe Probe Heatmap 3-25mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(PS1p.metrics, title = "PS1dE9 Probe Heatmap 3-25mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
par(mfrow = c(2, 4))
Rtrack::plot_density(wtpstd.metrics, title = "WT Probe Heatmap STD 3-25mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(dTgpstd.metrics, title = "dTg Probe Heatmap STD 3-25mo", col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(APPpstd.metrics, title = "APPswe Probe Heatmap STD 3-25mo", col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(PS1pstd.metrics, title = "PS1dE9 Probe Heatmap STD 3-25mo", col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(wtpenr.metrics, title = "WT Probe Heatmap ENR 3-25mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(dTgpenr.metrics, title = "dTg Probe Heatmap ENR 3-25mo", col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(APPpenr.metrics, title = "APPswe Probe Heatmap ENR 3-25mo", col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(PS1penr.metrics, title = "PS1dE9 Probe Heatmap ENR 3-25mo", col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
WT -> dTg -> APPswe -> PS1dE9 back to top
par(mfrow = c(1, 3))
###########WT_Probe##########
Rtrack::plot_density(wtpstd3.metrics, title = "WT Probe Heatmap STD 3mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(wtpstd14.metrics, title = "WT Probe Heatmap STD 13-14mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(wtpstd25.metrics, title = "WT Probe Heatmap STD 17-25mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(wtpenr3.metrics, title = "WT Probe Heatmap ENR 3mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(wtpenr14.metrics, title = "WT Probe Heatmap ENR 13-14mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(wtpenr25.metrics, title = "WT Probe Heatmap ENR 17-25mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
###########dTg_Probe##########
Rtrack::plot_density(dTgpstd3.metrics, title = "dTg Probe Heatmap STD 3mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(dTgpstd14.metrics, title = "dTg Probe Heatmap STD 13-14mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(dTgpstd25.metrics, title = "dTg Probe Heatmap STD 17-25mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(dTgpenr3.metrics, title = "dTg Probe Heatmap ENR 3mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(dTgpenr14.metrics, title = "dTg Probe Heatmap ENR 13-14mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(dTgpenr25.metrics, title = "dTg Probe Heatmap ENR 17-25mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
###########APP_Probe##########
Rtrack::plot_density(APPpstd3.metrics, title = "APPswe Probe Heatmap STD 3mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
#Rtrack::plot_density(APPpstd14.metrics, title = "APPswe Probe Heatmap STD 13-14mo",
# col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(APPpstd25.metrics, title = "APPswe Probe Heatmap STD 17-25mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(APPpenr3.metrics, title = "APPswe Probe Heatmap ENR 3mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
#Rtrack::plot_density(APPpenr14.metrics, title = "APPswe Probe Heatmap ENR 13-14mo",
# col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(APPpenr25.metrics, title = "APPswe Probe Heatmap ENR 17-25mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
###########PS1_Probe##########
Rtrack::plot_density(PS1pstd3.metrics, title = "PS1dE9 Probe Heatmap STD 3mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
#Rtrack::plot_density(PS1pstd14.metrics, title = "PS1dE9 Probe Heatmap STD 13-14mo",
# col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(PS1pstd25.metrics, title = "PS1dE9 Probe Heatmap STD 17-25mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(PS1penr3.metrics, title = "PS1dE9 Probe Heatmap ENR 3mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
#Rtrack::plot_density(PS1penr14.metrics, title = "PS1dE9 Probe Heatmap ENR 13-14mo",
# col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(PS1penr25.metrics, title = "PS1dE9 Probe Heatmap ENR 17-25mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
mo3=filter(Results, Age_group=='3')
mo14=filter(Results, Age_group=='13-14')
mo25=filter(Results, Age_group=='17-25')
mo3 %>%
mutate(mo3, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=path.length, fill=Condition))+geom_boxplot()+
labs(x="Day",
y="Average Path length",
title="Mean Path length 3mo mice")+scale_fill_manual(values = c("dTg_ENR" = "#ad5fc9", "dTg_STD" = "#bc91cc","WT_ENR" = "#6eca64", "WT_STD" = "#98cc93","APPswe_ENR" = "#d6564b", "APPswe_STD" = "#db867f","PS1dE9_ENR" = "#918730", "PS1dE9_STD" = "#c5b740"))
mo14 %>%
mutate(mo14, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=path.length, fill=Condition))+geom_boxplot()+
labs(x="Day",
y="Average Path length",
title="Mean Path length 13-14mo mice")+scale_fill_manual(values = c("dTg_ENR" = "#ad5fc9", "dTg_STD" = "#bc91cc","WT_ENR" = "#6eca64", "WT_STD" = "#98cc93","APPswe_ENR" = "#d6564b", "APPswe_STD" = "#db867f","PS1dE9_ENR" = "#918730", "PS1dE9_STD" = "#c5b740"))
mo25 %>%
mutate(mo25, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=path.length, fill=Condition))+geom_boxplot()+
labs(x="Day",
y="Average Path length",
title="Mean Path length 17-25mo mice")+scale_fill_manual(values = c("dTg_ENR" = "#6c7ed7", "dTg_STD" = "#909ef3","WT_ENR" = "#9f48a3", "WT_STD" = "#ce73cf","APPswe_ENR" = "#c85632", "APPswe_STD" = "#e9724b","PS1dE9_ENR" = "#9f9201", "PS1dE9_STD" = "#cab95b"))
Boxplot explanation:
Middle line in box -> Median
Box -> shows middle 50% of data(Distance between 1. and 3. Quartil)
Whisker(vertikal lines) -> show upper/lower 25% of data w/o outliers
Points -> outlier
mo3=filter(Results, Age_group=='3')
mo14=filter(Results, Age_group=='13-14')
mo25=filter(Results, Age_group=='17-25')
mo3 %>%
mutate(mo3, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=mean.velocity, fill=Condition))+geom_boxplot()+
labs(x="Day",
y="Average Velocity",
title="Mean Velocity 3mo mice")+scale_fill_manual(values = c("dTg_ENR" = "#ad5fc9", "dTg_STD" = "#bc91cc","WT_ENR" = "#6eca64", "WT_STD" = "#98cc93","APPswe_ENR" = "#d6564b", "APPswe_STD" = "#db867f","PS1dE9_ENR" = "#918730", "PS1dE9_STD" = "#c5b740"))
mo14 %>%
mutate(mo14, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=mean.velocity, fill=Condition))+geom_boxplot()+
labs(x="Day",
y="Average Velocity",
title="Mean Velocity 13-14mo mice")+scale_fill_manual(values = c("dTg_ENR" = "#ad5fc9", "dTg_STD" = "#bc91cc","WT_ENR" = "#6eca64", "WT_STD" = "#98cc93","APPswe_ENR" = "#d6564b", "APPswe_STD" = "#db867f","PS1dE9_ENR" = "#918730", "PS1dE9_STD" = "#c5b740"))
mo25 %>%
mutate(mo25, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=mean.velocity, fill=Condition))+geom_boxplot()+
labs(x="Day",
y="Average Velocity",
title="Mean Velocity 17-25mo mice")+scale_fill_manual(values = c("dTg_ENR" = "#6c7ed7", "dTg_STD" = "#909ef3","WT_ENR" = "#9f48a3", "WT_STD" = "#ce73cf","APPswe_ENR" = "#c85632", "APPswe_STD" = "#e9724b","PS1dE9_ENR" = "#9f9201", "PS1dE9_STD" = "#cab95b"))
par(mfrow = c(2, 2))
Rtrack::plot_strategies(Rtrack::threshold_strategies(strategies, 0.4), experiment = experiment,
factor = "All", exclude.probe = TRUE)